Mining high average-utility sequential rules to identify high-utility gene expression sequences in longitudinal human studies

2022 ◽  
pp. 116411
Author(s):  
Alberto Segura-Delgado ◽  
Augusto Anguita-Ruiz ◽  
Rafael Alcalá ◽  
Jesús Alcalá-Fdez
Keyword(s):  
Gene Expression ◽  
Human Studies ◽  
Average Utility ◽  
High Utility

scholarly journals Dynamic maintenance model for high average-utility pattern mining with deletion operation

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Qian Teng ◽  
Shahab Tayeb ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Pattern Mining ◽  
Computational Cost ◽  
Practical Applications ◽  
Itemset Mining ◽  
Dynamic Databases ◽  
Speed Up ◽  
Dynamic Maintenance ◽  
Average Utility ◽  
High Utility ◽  
Maintenance Model

AbstractThe high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility itemset mining (HUIM) for revealing the satisfied and interesting patterns. In practical applications, the database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed to handle the insertion process but fewer studies focused on processing the deletion process for knowledge maintenance. In this paper, we then develop a PRE-HAUI-DEL algorithm that utilizes the pre-large concept on HAUIM for handling transaction deletion in the dynamic databases. The pre-large concept is served as the buffer on HAUIM that reduces the number of database scans while the database is updated particularly in transaction deletion. Two upper-bound values are also established here to reduce the unpromising candidates early which can speed up the computational cost. From the experimental results, the designed PRE-HAUI-DEL algorithm is well performed compared to the Apriori-like model in terms of runtime, memory, and scalability in dynamic databases.

A Systematic Survey on High Utility Itemset Mining

2019 ◽  
Vol 18 (04) ◽  
pp. 1113-1185 ◽  
Author(s):  
Bahareh Rahmati ◽  
Mohammad Karim Sohrabi
Keyword(s):  
Data Structures ◽  
Search Space ◽  
Frequent Itemset ◽  
Itemset Mining ◽  
Efficient Data ◽  
Average Utility ◽  
High Utility ◽  
High Utility Itemsets ◽  
Downward Closure ◽  
Efficient Data Structures

High utility itemset mining considers unit profits and quantities of items in a transaction database to extract more applicable and more useful association rules. Downward closure property, which causes significant pruning in frequent itemset mining, is not established in the utility of itemsets and so the mining problem will require alternative solutions to reduce its search space and to enhance its efficiency. Using an anti-monotonic upper bound of the utility function and exploiting efficient data structures for storing and compacting the dataset to perform efficient pruning strategies are the main solutions to address high utility itemset mining problem. Different mining methods and techniques have attempted to improve performance of extracting high utility itemsets and their several variants, including high-average utility itemsets, top-k high utility itemsets, and high utility itemsets with negative values, using more efficient data structures, more appropriate anti-monotonic upper bounds, and stronger pruning strategies. This paper aims to represent a comprehensive systematic review for high utility itemset mining techniques and to classify them based on their problem-solving approaches.

scholarly journals Incrementally updating the high average-utility patterns with pre-large concept

2020 ◽  
Vol 50 (11) ◽  
pp. 3788-3807
Author(s):  
Jerry Chun-Wei Lin ◽  
Matin Pirouz ◽  
Youcef Djenouri ◽  
Chien-Fu Cheng ◽  
Usman Ahmed
Keyword(s):  
State Of The Art ◽  
The State ◽  
Batch Mode ◽  
Itemset Mining ◽  
The Past ◽  
Dynamic Databases ◽  
Speed Up ◽  
Average Utility ◽  
High Utility ◽  
High Utility Patterns

Abstract High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the size of the itemset, thus providing a more balanced scale to measure the average-utility for decision-making. Several algorithms were presented to efficiently mine the set of high average-utility itemsets (HAUIs) but most of them focus on handling static databases. In the past, a fast-updated (FUP)-based algorithm was developed to efficiently handle the incremental problem but it still has to re-scan the database when the itemset in the original database is small but there is a high average-utility upper-bound itemset (HAUUBI) in the newly inserted transactions. In this paper, an efficient framework called PRE-HAUIMI for transaction insertion in dynamic databases is developed, which relies on the average-utility-list (AUL) structures. Moreover, we apply the pre-large concept on HAUIM. A pre-large concept is used to speed up the mining performance, which can ensure that if the total utility in the newly inserted transaction is within the safety bound, the small itemsets in the original database could not be the large ones after the database is updated. This, in turn, reduces the recurring database scans and obtains the correct HAUIs. Experiments demonstrate that the PRE-HAUIMI outperforms the state-of-the-art batch mode HAUI-Miner, and the state-of-the-art incremental IHAUPM and FUP-based algorithms in terms of runtime, memory, number of assessed patterns and scalability.

scholarly journals Analytics of high average-utility patterns in the industrial internet of things

2021 ◽  
Author(s):  
Jimmy Ming-Tai Wu ◽  
Zhongcui Li ◽  
Gautam Srivastava ◽  
Unil Yun ◽  
Jerry Chun-Wei Lin
Keyword(s):  
High Performance ◽  
Pattern Mining ◽  
Search Space ◽  
Research Field ◽  
Industrial Internet Of Things ◽  
Utility Measure ◽  
Itemset Mining ◽  
Industrial Internet ◽  
Average Utility ◽  
High Utility

AbstractRecently, revealing more valuable information except for quantity value for a database is an essential research field. High utility itemset mining (HAUIM) was suggested to reveal useful patterns by average-utility measure for pattern analytics and evaluations. HAUIM provides a more fair assessment than generic high utility itemset mining and ignores the influence of the length of itemsets. There are several high-performance HAUIM algorithms proposed to gain knowledge from a disorganized database. However, most existing works do not concern the uncertainty factor, which is one of the characteristics of data gathered from IoT equipment. In this work, an efficient algorithm for HAUIM to handle the uncertainty databases in IoTs is presented. Two upper-bound values are estimated to early diminish the search space for discovering meaningful patterns that greatly solve the limitations of pattern mining in IoTs. Experimental results showed several evaluations of the proposed approach compared to the existing algorithms, and the results are acceptable to state that the designed approach efficiently reveals high average utility itemsets from an uncertain situation.

Abstract 14427: Differences in Molecular Phenotype in Mouse and Human Hypertrophic Cardiomyopathy Suggest Need for Precision Medicine Approach to Treatment Based on Human Studies

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Styliani Vakrou ◽  
Yamin Liu ◽  
Li Zhu ◽  
Yufan Guan ◽  
Ryuya Fukunaga ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hypertrophic Cardiomyopathy ◽  
Precision Medicine ◽  
Mirna Expression ◽  
Intracellular Signaling ◽  
Human Studies ◽  
Disease Stage ◽  
Specific Gene ◽  
Established Disease ◽  
Allele Specific

Background: Mouse studies at early stage of hypertrophic cardiomyopathy (HCM) indicate allele-specific differences in cardiac gene expression and mitochondrial function. But data at established disease stage in mouse and human HCM are lacking. We hypothesized that 1) allele-specific differences persist at established stage, and 2) mouse and human HCM have distinct molecular biosignatures. Methods: We analyzed the transcriptome (mRNA, miRNA) in 2 HCM mouse models (R92W-TnT, R403Q-MyHC)/littermate controls at 24weeks of age and in human myectomy samples/healthy-control hearts ( GSE36961, GSE36946 ). We examined myocyte redox, mitochondrial DNA copy number (mtDNA-CN), respiration, ROSgeneration/scavenging and Ca 2+ handling in mutant/littermate-control mice . Results: Analysis of mRNA/miRNA expression and Ingenuity Pathway Analysis (IPA) revealed distinct allele-specific gene expression in mouse HCM and marked differences from human HCM. Only CASQ1 and GPT1 were similarly regulated in both mouse HCM/human HCM. KEGG analysis revealed enrichment of several metabolic pathways, but only pyruvate metabolism was enriched in both mouse HCM/human HCM. IPA predicted upregulation of 2 pathways (inflammasome, type 2 diabetes signaling) in MyHC mutants, and upregulation of 18 pathways (including STAT3, ILK, Ca 2+ signaling) in TnT mutants; the anti-hypertrophic/anti-fibrotic LXR/RXR pathway was the most upregulated in human HCM. Losartan was a predicted therapy only in TnT mutants. Myocytes of both mutant mice exhibited an oxidized redox environment. Mitochondrial complex I respiration was lower in both mutants compared to controls. MyHC mutants had similar mtDNA-CN and mitochondrial Ca 2+ handling, but TnT mutants demonstrated lower mtDNA-CN and impaired mitochondrial Ca 2+ handling, compared to respective controls. Conclusions: Molecular profiling reveals allele-specific differences in mRNA/miRNA expression, intracellular signaling and mt-function/number in mouse HCM at established disease stage. Transcriptional differences between mouse and human HCM highlight the need for precision medicine approaches based on human studies.

Efficient Method for Mining High-Utility Itemsets Using High-Average Utility Measure

2020 ◽  
pp. 305-315
Author(s):  
Loan Thi Thuy Nguyen ◽  
Trinh D. D. Nguyen ◽  
Anh Nguyen ◽  
Phuoc-Nghia Tran ◽  
Cuong Trinh ◽  
...  
Keyword(s):  
Efficient Method ◽  
Utility Measure ◽  
Average Utility ◽  
High Utility ◽  
High Utility Itemsets

scholarly journals The epigenetics of perinatal stress

2019 ◽  
Vol 21 (4) ◽  
pp. 369-378 ◽  
Keyword(s):  
Gene Expression ◽  
Mental Health ◽  
Early Life ◽  
The Body ◽  
Human Studies ◽  
Attractive Potential ◽  
Epigenetic Mechanisms ◽  
Behavioral Phenotypes ◽  
Peripheral Tissues ◽  
The Impact

Early life adversity is associated with long-term effects on physical and mental health later in life, but the mechanisms are yet unclear. Epigenetic mechanisms program cell-type-specific gene expression during development, enabling one genome to be programmed in many ways, resulting in diverse stable profiles of gene expression in different cells and organs in the body. 􀀧NA methylation, an enzymatic covalent modification of 􀀧NA, has been one of the principal epigenetic mechanisms investigated. Emerging evidence is consistent with the idea that epigenetic processes are involved in embedding the impact of early-life experience in the genome and mediating between social environments and later behavioral phenotypes. Whereas there is evidence supporting this hypothesis in animal studies, human studies have been less conclusive. A major problem is the fact that the brain is inaccessible to epigenetic studies in humans and the relevance of DNA methylation in peripheral tissues to behavioral phenotypes has been questioned. In addition, human studies are usually confounded with genetic and environmental heterogeneity and it is very difficult to derive causality. The idea that epigenetic mechanisms mediate the life-long effects of perinatal adversity has attractive potential implications for early detection, prevention, and intervention in mental health disorders will be discussed.

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